Generating high-resolution concentration maps for atmospheric gases using geography-informed machine learning

ABSTRACT

Generating one or more high-resolution atmospheric gas concentration maps using geography-informed machine learning includes obtaining a remote sensing dataset constrained by at least one temporal window and at least one spatial window defining a first geographic area. The remote sensing dataset includes at least a first set of atmospheric gas concentration data for a plurality of atmospheric gases. A training dataset is generated based on the remote sensing dataset. A machine learning model is trained with the training dataset to predict a plurality of atmospheric gas concentration values for at least one atmospheric gas of the plurality of atmospheric gases in a given geographic area and with a spatial resolution that is greater than a spatial resolution of atmospheric gas concentration data provided as an input to the machine learning module.

TECHNICAL FIELD

The present invention is directed to systems and methods for generatinghigh-resolution concentration maps for atmospheric gases usinggeography-informed machine learning.

BACKGROUND

As more policies are stipulated for air quality and climate change,there is an increasing need to monitor atmospheric gases, such asnitrogen dioxide (NO2). Monitoring atmospheric gases provides importantdata from air quality and public health perspectives, such asatmospheric gas concentrations, the location of “hot spots”, and thelike. One technique for monitoring NO2 and other atmospheric gasesincludes using ground sensors and on-demand measurements, such as cars,drones, or aircraft equipped with gas sensors. Although the use ofground sensors and on-demand measurements for monitoring NO2 and otheratmospheric gases may reliably quantify surface-level atmospheric gases,this technique typically does not provide dense sensor networks, whichare needed for developing fine-scale maps gas concentrations. Also,ground sensor observations are difficult to scale to larger regions.Another technique for monitoring atmospheric gas concentrations includesindirect observation through accounting models. In this technique,modelers use, for example, traffic datasets as a proxy to measureatmospheric gas concentration, and the like. However, with increasingadoption of electric vehicles and other types of zero-emission vehicles,there is a risk of divergence from the metrics provided by thistechnique and a possibility of providing misleading gas concentrations.Also, this technique typically requires surveys of the land along withhuman labeling, which can be expensive, time consuming, and prone tohuman error.

BRIEF SUMMARY

In one embodiment, a method for generating one or more high-resolutionatmospheric gas concentration maps using geography-informed machinelearning includes: obtaining a remote sensing dataset constrained by atleast one temporal window and at least one spatial window defining afirst geographic area, the remote sensing dataset comprising at least afirst set of atmospheric gas concentration data for a plurality ofatmospheric gases; generating a training dataset based on the remotesensing dataset; and training a machine learning model with the trainingdataset to predict a plurality of atmospheric gas concentration valuesfor at least one atmospheric gas of the plurality of atmospheric gasesin a given geographic area and with a spatial resolution that is greaterthan a spatial resolution of atmospheric gas concentration data providedas an input to the machine learning module.

In another embodiment, an information processing system for generatingone or more high-resolution atmospheric gas concentration maps usinggeography-informed machine learning includes: a processor; memorycommunicatively coupled to the processor; and an atmospheric gas mappingunit communicatively coupled to the processor and the memory. Theatmospheric gas mapping unit obtains a remote sensing datasetconstrained by at least one temporal window and at least one spatialwindow defining a first geographic area. The remote sensing datasetcomprising at least a first set of atmospheric gas concentration datafor a plurality of atmospheric gases. The atmospheric gas mapping unitfurther generates a training dataset based on the remote sensingdataset, and trains a machine learning model with the training datasetto predict a plurality of atmospheric gas concentration values for atleast one atmospheric gas of the plurality of atmospheric gases in agiven geographic area and with a spatial resolution that is greater thana spatial resolution of atmospheric gas concentration data provided asan input to the machine learning module.

In a further embodiment, a method for generating one or morehigh-resolution atmospheric gas concentration maps usinggeography-informed machine learning includes: obtaining a remote sensingdataset constrained by at least one temporal window and at least onespatial window defining a first geographic area, the remote sensingdataset comprising at least a first set of atmospheric gas concentrationdata for a plurality of atmospheric gases; generating a training datasetbased on the remote sensing dataset; training a machine learning modelwith the training dataset; and processing, by the trained machinelearning model, an input dataset comprising a second set of atmosphericgas concentration data for at least one atmospheric gas of the pluralityof atmospheric gases, the second set of atmospheric gas concentrationdata being associated with a second geographic area and having a firstspatial resolution; and predicting, by the trained machine learningmodule based on processing the input dataset, a plurality of atmosphericgas concentration values for the at least one atmospheric gas, whereinthe plurality of predicted atmospheric gas concentration. values has asecond spatial resolution that is greater than the first spatialresolution.

BRIEF DESCRIPTION OF THE DRAWINGS

The foregoing and other features and advantages of the subject matterdisclosed herein will be made apparent from the following detaileddescription taken in conjunction with the accompanying drawings, inwhich:

FIG. 1 is block diagram illustrating one example of an operatingenvironment for generating high-resolution atmospheric gas maps usinggeography-information machine learning in accordance with one or moreembodiments;

FIG. 2 is block diagram illustrating a detailed view of a mapping systemfor generating high-resolution atmospheric gas maps usinggeography-information machine learning in accordance with one or moreembodiments;

FIG. 3 illustrates a low-resolution atmospheric gas map generated by aremote sensing device, such as a satellite, and a high-resolutionatmospheric gas map generated by the mapping system of FIG. 2 inaccordance with one or more embodiments;

FIG. 4 and FIG. 5 are operational flow diagrams together illustratingone example method of generating high-resolution atmospheric gas mapsusing geography-information machine learning in accordance with one ormore embodiments; and

FIG. 6 is a block diagram illustrating one example of an informationprocessing system according to one embodiment of the present disclosure.

DETAILED DESCRIPTION

Given the drawbacks of conventional atmospheric gas measurementtechniques, satellite remote sensing is a promising alternative for ofmeasuring atmospheric gases. However, although public satellites cancapture larger areas in one swath, the best spatial resolution foratmospheric gases currently available remains coarse (approximately 3kilometer×3 kilometer (km)) and generally does not provide enoughgranular data to accurately determine how implemented policies impactthe environment or regulate bad actors. As such, the techniques of oneor more embodiments described herein rapidly generate near real-time,high spatial resolution atmospheric gas concentration data using acombination of satellite data, such as atmospheric monitoring,multispectral, synthetic aperture radar, visual infrared imagery, thelike, or a combination thereof. A geography-informed machine learning(ML) system/unit of one or more embodiments takes the obtained satellitedata as input and rapidly generates fine-scale atmospheric gas (e.g.,NO2) maps for any region of interest without any field work or manuallabeling effort, unlike conventional techniques. The fine-scaleatmospheric gas maps generated by one or more embodiments has a higherspatial resolution (e.g., less than 1 km×1 km) than the input satellitedata.

FIG. 1 illustrates one example of an operating environment 100 forgenerating high-resolution atmospheric gas maps usinggeography-information machine learning. In one or more embodiments, theoperating environment 100 comprises one or more information processingsystems 102 and one or more user systems 104 communicatively coupled toat least one network 106. Examples of an information processing system102 include a server (local or remote/cloud-based), a workstation, adesktop computer, a portable computing system such as a laptop, ahandheld computing device such as cellular mobile device or a tablet, orthe like. Examples of a user system 104 include a portable computingsystem such as a laptop, a handheld computing device such as cellularmobile device or a tablet computing device, a wearable device such as asmart watch, or the like. The network(s) 106, in one or moreembodiments, comprises cloud and/or non-cloud based technologies, alocal area network (LAN), a general wide area network (WAN), publicnetworks such as the Internet, private wireless communication networks,non-cellular networks such as Wireless Fidelity (Wi-Fi) networks, and/orthe like.

The operating environment 100 further comprises one or more remotesensing devices/systems 108 (illustrated as remote sensing devices 108-1to 108-4), such as artificial satellites, manned aerial vehicles,unmanned aerial vehicles, or the like. The remote sensing devices 108are configured to collect remote sensing data 110 (illustrated as remotesensing data 110-1 to 110-4). In one example, a remote sensing device108 (e.g., an unmanned aerial vehicle) collects the remote sensing data110 while operating within the Earth's atmosphere. In another example, aremote sensing device 108 (e.g., a satellite) collects the remotesensing data 110 while operating above/outside the Earth's atmosphere.

Examples of remote sensing data 110 include multispectral imagingspectrometry data, multispectral data, synthetic aperture radar (SAR)data, visible infrared imaging radiometer Suite (VIIRS) data (e.g.,nighttime light data), the like, or a combination thereof. Multispectralimaging spectrometry data indicates the concentrations of gases andother pollutants in the atmosphere, such as nitrogen dioxide, ozone,formaldehyde, sulfur dioxide, methane, carbon monoxide, aerosols, andthe like. One example of multispectral imaging spectrometry data is thedataset collected by the Sentinel-5P satellite, which takes measurementsin the ultraviolet, visible, near and short-wavelength infrared lightspectrum. These measurements can be combined with auxiliary input data(e.g., air pressure, snow/ice masks, etc.) to model the concentration ofeach atmospheric gas based on their absorption characteristics atspecific wavelengths of the light. Multispectral imaging spectrometrydata includes different bands of atmospheric gas data, such asatmospheric gas column observation, which represents a concentration ofthe atmospheric gas, such as NO2, from the Earth's surface to the top ofthe Earth's atmosphere. Multispectral imaging spectrometry datatypically has a maximum spatial resolution of between 3 km and 7 km.

Multispectral data captured by the remote sensing devices 108 includesdata collected across multiple spectral channels. One example ofmultispectral data is the dataset collected by the Sentinel-2satellites. The Sentinel-2 multispectral data is collected acrossthirteen spectral bands including visible and near infrared (NIR) bands,red edge bands, short wave infrared (SWIR) bands, and atmospheric bands.Another example, of multispectral data is the dataset collected by theLandsat-8 satellite. The Landsat-8 multispectral data is collectedacross eleven spectral bands including visible and NIR bands, SWIRbands, a panchromatic band, thermal infrared bands, a coastal/aerosolband, and a cirrus band. Multispectral data captured by the remotesensing devices 108 (e.g., satellites) can be used for variousapplications including monitoring plant growth, monitoring surfacetemperature characteristics, land cover classification, land useclassification, land mapping, monitoring water quality, surfaceemergencies management, geology applications, and the like.Multispectral data typically has a maximum spatial resolution between 10meters to 30 meters (m).

SAR data typically comprises two-dimensional images or three-dimensionalreconstructions of objects, such as landscapes, and is used for marinemonitoring, land monitoring, emergency response, and the like. Oneexample of SAR data is the dataset collected or generated by theSentinel-1 satellite. The maximum spatial resolution of SAR data isusually between 10 m and 60 m. VIIRS data typically comprises imageryand radiometric measurements of the land, atmosphere, cryosphere, andoceans in the visible and infrared bands of the electromagneticspectrum. VIIRS data includes nighttime light data includes measurementsof nocturnal visible and near infrared light and is typically used forestimating population, assessing electrification of remote areas,monitoring disasters and conflict, understanding biological impacts ofincreased light population, and the like. One example of nighttime lightdata is the panchromatic Day/Night band (DNB) data collected by theSuomi National Polar-orbiting Partnership (NPP) and the National Oceanicand Atmospheric Administration-20 (NOAA-20) satellites. The Day/Nightband (DNB) data is collected by the visible infrared imaging radiometersuite (VIIRS) sensors implemented by the Suomi and NOAA-20 satellites,which captures data in 22 different spectral bands. The maximumresolution of the DNB data is typically 750 m. Although one or moreembodiments described herein use satellites as one example of remotesensing devices 108, other types of remote sensing devices 108 areapplicable as well. For example, devices, such as manned or unmannedaerial vehicles, capable of performing remote measurement/collection ofthe various types of data described herein from within or above theEarth's atmosphere are applicable as well.

As described in greater detail below with respect to FIG. 2 , theinformation processing system 102 comprises a high-resolutionatmospheric gas mapping system/unit 112 (referred to as “mapping system112” or “mapping unit 112” for brevity) that includes, for example, ageography informed machine learning (GIML) module 114. The mappingsystem 112 takes the remote sensing data 110 generated by one or more ofthe remote sensing devices 108 as input and uses the GIML module 114 torapidly generate/predict fine-scale (e.g., less than or equal to 500meter×500 meter resolution) atmospheric gas maps 116 for any region ofinterest based on this input without any field work or manual labelingeffort. Stated differently, the GIML module 114 takes as input dataincluding atmospheric data gas data having a given spatial resolutionand outputs data points including predicted gas concentration valueshaving a spatial resolution that is greater than the spatial resolutionof the input atmospheric data gas data. As used herein, lower spatialresolution means less detail and larger pixel or grid cell size comparedto higher spatial resolution, which means more detail and a smallerpixel or grid cell size.

The information processing system 102 obtains/accesses the remotesensing data 110 directly from the remote sensing device(s) 108, throughthe network(s) 106, from one or more servers (not shown), the like, or acombination thereof. One or more types of remote sensing data 110, suchas multispectral data, can be obtained by the information processingsystem 102 from a single remote sensing device 108 or from multipleremote sensing device 108 (e.g., the Sentinel-2 and Landsat-8satellites) to mitigate temporal gaps in the multispectral data. In oneor more embodiments, at least a portion of the remote sensing data 110is obtained by the information processing system 102 asmulti-dimensional (multi-band) imagery data (raster/map). A rastercomprises, for example, a matrix of cells/pixels organized into rows andcolumns (or a grid) with each cell/pixel including one or more valuesrepresenting information, such as atmospheric gas column data,multispectral data, or the like.

FIG. 2 shows a more detailed view of the mapping system 112 implementedby the information processing system 102. In one or more embodiments,the mapping system 112 comprises the GIML module 114, a firstpre-processing module 202, a second pre-processing module 204, apost-processing module 206, and one or more generated/predictedhigh-resolution atmospheric gas maps 116. Although FIG. 2 shows multiplecomponents/modules of the mapping system 112 as being separate from eachother, two or more of these components/modules can be combined. Also, inone or more embodiments, the components of the mapping system 112 resideon a single information processing system 102. However, in otherembodiments, at least one component of the mapping system 112 resides ona different information processing system than the remaining componentsof the mapping system 112. Also, the mapping system 112 or one or morecomponents of the mapping system 112 illustrated in FIG. 2 areimplemented as hardware/circuitry, software, or a combination thereof.

The pre-processing modules 202 and 204 are implemented by the mappingsystem 112 to generate/obtain training data for one or more ML models232-1 and to generate/obtain runtime (operational) data that is inputinto the trained ML models 232-1. To generate the training and thetraining data and operational data, the first pre-processing module 202takes remote sensing data 110, including atmospheric gas column(observation) data 208 and multispectral data 210 (illustrated asmultispectral data 210-1 and multispectral satellite data 210-2), asinput. The second pre-processing module 204 takes remote sensing data110 including SAR data 212 and VIIRS data 214 as input. In one or moreembodiments, one or more of the atmospheric gas column data 208,multispectral data 210, SAR data 212, and VIIRS data 214 are representedas multi-dimensional (multi-band) imagery data (raster/map), asdescribed above.

Each of the first pre-processing module 202 and the secondpre-processing module 204 implement one or more temporal windows 216(illustrated as temporal windows 216-1 and 216-2) and one or morespatial windows 218 (illustrated as spatial windows 218-1 and 218-2). Inone or more embodiments, the temporal windows 216 and spatial windows218 are defined such that the first pre-processing module 202 and thesecond pre-processing module 204 each aggregate their remote sensingdata 110 spatiotemporally across different spatial resolutions. In oneexample, the spatial windows 218 are defined based on theregion-of-interest (ROI) for which the mapping system 112 is togenerate/predict a high-resolution atmospheric gas map(s) 116. Forexample, the spatial window 218 is defined by the size andlatitude/longitude coordinates of the ROI. Also, in one or moreembodiments, the spatial windows 218 are defined with a window size thatprovides for sufficient variation in data collected by the remotesensing devices 108. For example, a spatial window 218 can be definedwith a window size of 30 km×30 km or greater, although smaller windowsizes are applicable as well.

In one or more embodiments, temporal windows 216 are defined such thatthe first pre-processing module 202 and second pre-processing module 204process/aggregate remote sensing data 110 collected by the remotesensing devices 108 over a sufficient period of time that providesmeaningful data for performing the operations described herein. Forexample, if the mapping system 112 is configured to processmultispectral data 210 for the ROI from two or more remote sensingdevices 108 (e.g., satellites), the temporal window 216-1 is definedsuch that the multispectral data 210 processed/aggregated by the firstpre-processing system 202 includes multispectral data 210 collected byeach of the two or more remote sensing devices 108. Stated differently,the first pre-processing system 202 aggregates the input multispectraldata 210 collected by the remote sensing devices 108 during a timeperiod that is long enough to include multispectral data 210 collectedby each of the two or more remote sensing devices 108. If the temporalwindow 216-1 is too small/short, the aggregated multispectral data 210may not include data collected from all the remote sensing devices 108of interest. One example of a temporal window 216-1 for multispectraldata 210 is 5 days, although a lesser or greater amount of time isapplicable as well.

The temporal window 216-2 for SAR data 212 and VIIRS data 214 can be thesame or different from the temporal window 216-1 defined for atmosphericgas column data 208 and multispectral data 210. In one or moreembodiments, SAR data 212 and VIIRS data 214 are used by the mappingsystem 112 to capture the underlying spatial structure of the ROI, andthe changes in these datasets across time are minimal. As such, thetemporal window 216-2 constraint for SAR data 212 and VIIRS data 214 canbe relaxed compared to the temporal window 216-1 constraint foratmospheric gas column data 208 and multispectral data 210. One exampleof a temporal window 216-2 for SAR data 212 and VIIRS data 214 isbetween 15 and 30 days, although a lesser or greater amount of time isapplicable as well.

When processing/aggregating the remote sensing data 110, the firstpre-processing module 202 and the second pre-processing module 204 eachprocess/aggregate their respective remote sensing data 110 over the timeperiod defined by their respective temporal window(s) 216 for thespatial area associated with the ROI and constrained by the spatialwindow 218. For example, if the temporal window 216-1 defined foratmospheric gas column data 208 and multispectral data 210 is 5 days andthe spatial window for the ROI is defined as 30 km×30 km, the firstpre-processing module 202 aggregates atmospheric gas column data 208 andmultispectral data 210 for the 30 km×30 km ROI area generated/collectedby the remote sensing device(s) 108 over the 5 day period defined by thetemporal window 216-1. Stated differently, the first pre-processingmodule 202 processes the atmospheric gas column data 208 andmultispectral data 210 to identify and aggregate portions of theatmospheric gas column data 208 and multispectral data 210 associatedwith the defined 30 km×30 km area and collected over the define 5 dayperiod. A similar process is performed by the second pre-processingmodule 204 for the SAR data 212 and VIIRS data 214.

As described above, the atmospheric gas column data 208 andmultispectral data 210 can be obtained/accessed by the firstpre-processing module 202 as multi-dimensional (multi-band) imagery data(raster/map). In these embodiments, the first pre-processing module 202aggregates rasters generated by the remote sensing devices 108representing atmospheric gas column data 208 and multispectral data 210over the temporal window 216-1 and spatial window 218-1, and the secondpre-processing module 202 aggregates rasters generated by the remotesensing devices 108 representing SAR data 212 and VIIRS 214 over thetemporal window 216-2 and spatial window 218-2. In one or moreembodiments, the pre-processing modules 202 comprises an overlay andextraction (OE) module 220 (illustrated as OE module 220-1 and OE module220-2) that extracts remote sensing data 110 of interest from therasters (or other representations of the remote sensing data 110). Inother embodiments, the mapping system 112 includes a single OE module220 that extracts remote sensing data 110 of interest from the remotesensing data 110 aggregated by both the first pre-processing module 202and the second pre-processing module 202.

In one or more embodiments, the OE module 220-1 of the firstpre-processing module 202 spatially aligns the rasters aggregated by thefirst pre-processing module respectively comprising the atmospheric gascolumn data 208, the first set of multispectral data 210-1, and thesecond set of multispectral data 210-2. The OE module 220-s of thesecond pre-processing module 204 spatially aligns the rasters aggregatedby the second pre-processing module respectively comprising the SAR data212 and the VIIRS data 214. In other embodiments, the rasters aggregatedby both the first pre-processing module 202 and the secondpre-processing module are spatially aligned together.

The OE modules 220 then extract data points of interest from thespatially aligned rasters. In one or more embodiments, the OE module220-1 of the first pre-processing module 202 extracts atmospheric gascolumn data 208 for a gas(es) of interest and multispectral data 210from the spatially aligned rasters for the ROI. For example, if themapping system 112 is interested in atmospheric NO2, the OE module 220-1extracts NO2 data, such as the total vertical column of NO2 (i.e., theratio of the slant column density of NO2 and the total air mass factor),for each pixel/cell defining the ROI within the aggregated rasters.Examples of multispectral data extracted for each pixel/cellrepresenting the ROI within the aggregated rasters (or otherrepresentation of the multispectral data 210) is shown below in Table 1.In this example, the multispectral bands are ordered by their featureimportance. However, in other examples, the ordering does not reflectfeature importance, or reflects a different configuration of featureimportance. In one or more embodiments, the data extracted by the OEmodule 220-1 of the first pre-processing module 202 is represented as aset of data points, wherein each data point of the set of data pointsincludes (or is associated with) latitude and longitude coordinates,atmospheric gas column data for the latitude and longitude coordinatesof the data point, and multispectral band data for the latitude andlongitude coordinates of the data point.

TABLE 1 Mean Wavelength Satellite Band (nanometer) DescriptionSentinel-2 B5 740.2 Red Edge 1 Landsat-8 B3 560 Green Sentinel-2 B122162.4 Shortwave Infrared 2 Landsat-8 B5 865 Near infrared Sentinel-2 B8835.1 Near infrared Landsat-8 B10 1089.5 Thermal Infrared 1 Sentinel-2B4 664.5 Red Landsat-8 B8 590 Panchromatic Sentinel-2 B10 1373.5 CirrusLandsat-8 B7 2160 Shortwave Infrared 2 Sentinel-2 B9 945 Water Vapor

The OE module 220-2 of the second pre-processing module 202 extracts oneor more of SAR data 212 and VIIRS data 214 of interest from thespatially aligned rasters for the ROI. For example, the OE module 220-2extracts data provided by at least two backscatter bands from the SARdata 212 and/or nighttime radiance values (e.g., Day/Night band (DNB)data) from the VIIRS data 214. The SAR backscatter bands include avertical transmit/vertical receive (VV) band and a verticaltransmit/horizontal receive (VH) band. In the VV band, the remotesensing device 108 (e.g., Sentinel-1 satellite) transmits and receives alongitudinal electromagnetic wave with vertical polarization. In the VHband, the remote sensing device 108 transmits a longitudinalelectromagnetic wave with vertical polarization but receives the wavewith horizontal polarization. Accordingly, in one or more embodiments,the extracted SAR data 212 includes VV and VH band data, such as singleco-polarization vertical transmit/vertical receive data and dual-bandcross-polarization vertical transmit/horizontal receive data. In one ormore embodiments, the data extracted by the OE module 220-2 of thesecond pre-processing module 204 is represented as a set of data points,wherein each data point of the set of data points includes (or isassociated with) latitude and longitude coordinates, SAR data for thelatitude and longitude coordinates of the data point, and nighttimeradiance values for the latitude and longitude coordinates of the datapoint.

In one or more embodiments, the OE module(s) 220 extract the atmosphericgas column data 208, multispectral data 210, SAR data 212, and/or VIIRSdata 214 from the spatially aligned rasters by generating two sets ofrandom points at varying densities over the spatial window 218representing the ROI. In embodiments implementing a separate OE module220 for each pre-processing module 202 and 204, the OE modules 220 arein communication with each other such that they extract their data atthe same random points. For example, one of the OE modules 220-1 cangenerate the random points and inform the other OE module 220-2 of thelatitude and longitude coordinates of these points.

The first set of random points is referred to as a sparse point set andthe second set of random points is referred to as a dense point set. Thedistance between the points included in the sparse point is greater thanthe represented distance between the points in the dense point set. Forexample, the sparse point set can include at least one point set every 3km and the dense point set can include at least one point every 100meters, although lesser or greater distances are applicable as well.Also, each point in the sparse point set and dense point set isassociated with latitude and longitude coordinates within the spatialwindow 218-1.

For each point in the sparse point set and dense point set, the OEmodule(s) 220 extracts the atmospheric gas column data 208,multispectral data 210, SAR data 212, and/or VIIRS data 214 of interestat the respective locations identified by the point's latitude andlongitude. The atmospheric gas column data 208, multispectral data 210,SAR data 212, and/or VIIRS data 214 extracted for the sparse point setare merged as a single dataset referred to as a sparse extractiondataset 222. Similarly, the atmospheric gas column data 208,multispectral data 210, SAR data 212, and/or VIIRS data 214 extractedfor the dense point set are merged as a single dataset referred to as adense extraction dataset 224. The mapping system 112 generates thesparse extraction dataset 222 and the dense extraction dataset 224 inparallel or at different times. For example, after the GIML module 114has been trained using the sparse extraction dataset 222, the firstpre-processing module 202 can generate the dense extraction dataset 224in response to the mapping system 112 receiving a request to generate apredicted high-resolution atmospheric gas map 116.

In one or more embodiments, prior to the extracted SAR data 212 and/orVIIRS data 214 being merged as part of the sparse extraction dataset 222and the dense extraction dataset 224, a spatial autocorrelation module226 (illustrated as spatial autocorrelation module 226-1 and spatialautocorrelation module 226-2) processes the extracted SAR data 212and/or VIIRS data 214 for each point of the sparse point set and thedense point set, respectively. For example, the spatial autocorrelationmodule 226 processes the extracted SAR data 212 and VIIRS data 214(e.g., nighttime radiance values) for each point of the sparse point setto generate a spatial association indicator, such as a local Moran's Iindex. A similar process is performed for the extracted SAR data 212 andVIIRS data 214 (e.g., nighttime radiance values) for each point of thedense point set. A spatial association indicator is a statistic thatevaluates the existence of clusters in the spatial arrangement of agiven variable. The Moran's Index, also referred to as “Moran's I” is acorrelation coefficient used in geography that measures the spatialautocorrelation of the dataset, i.e., the index estimates whether thegiven spatial data has patterns or clusters. See, for example, A. Getis,“A History of the Concept of Spatial Autocorrelation: A Geographer'sPerspective,” Geographical Analysis, pp. 297-309, 2008, which is herebyincorporated by reference in its entirety. While the global coefficientis a single value, the local Moran's I calculates the spatialautocorrelation at fixed distances or neighborhoods. Although at leastsome embodiments implement local Moran's I as an indicator of spatialassociation, other indicators of spatial association are applicable awell.

In one or more embodiments, the spatial autocorrelation module(s) 226performs separate local spatial autocorrelation operations for the SARdata 212 and VIIRS data 214 (e.g., nighttime radiance values) extractedfor data points of the sparse point set and the data points of the densepoint set. For example, the spatial autocorrelation module(s) 226calculates a local Moran's I for a given number (e.g., 8) of nearestneighbors of each data point of the sparse point set and separatelycalculates a local Moran's I for a given number (e.g., 8) of nearestneighbors of each data point of the dense point set. The local Moran's Ioperation(s) generates clusters that capture the urban areas, populationdensity, waterbodies, and other land uses without explicitly labelingthem.

As such, the output of the spatial autocorrelation module(s) 226 is aspatial autocorrelated structure(s) 228 comprising a land usecategorization/classification (based on the extracted SAR data 212)and/or a geo-physical/socio-economic classification (based on theextracted VIIRS data 214) for each data point in the sparse data pointset and each data point in the dense data point set. The secondpre-processing module 204 (or another module) generates the sparseextraction dataset 222 by merging the spatial autocorrelation data inautocorrelated structure(s) 228 generated for the sparse point set withthe atmospheric gas column data 208 and multispectral data 210 extractedfor the parse point set. A similar process is performed for generatingthe dense extracted data set 224. As such, each of the sparse extractiondataset 222 and the dense extraction dataset 224 each comprise aplurality of data points, wherein each data point is associated withlatitude and longitude coordinates, atmospheric gas column data for thelatitude and longitude coordinates of the data point, multispectral banddata for the latitude and longitude coordinates of the data point, and aland categorization/classification and/or a geo-physical/socio-economicclassification for the latitude and longitude coordinates of the datapoint.

In one or more embodiments, an ML training module 230 of the mappingsystem 112 uses the sparse extraction dataset 222 to train one or moreML models 232-1-1 implemented by the GIML 114 to generate a set of datapoints with predicted atmospheric gas column density/concentrationvalues. The ML training module 230 also selects a subset of the sparseextraction data 222 as a validation dataset to validate the ML model(s)232-1-1 during training. The validation dataset is not observed by theML model(s) 232-1-1 during training. In one or more embodiments, thevalidation dataset includes atmospheric column data 208, multispectraldata 210, and a spatial autocorrelation structure(s)/data 228 associatedwith the same geographical area as the training dataset. However, inother embodiments, the validation dataset includes atmospheric columndata 208, multispectral data 210, and spatial autocorrelationstructure(s)/data 228 associated with a different geographical area asthe training dataset

The ML training module 230, in one or more embodiments, uses thespectral bands (e.g., see Table 1) and the spatial autocorrelation dataincluded in the sparse extraction data 222 as input explanatoryfeatures. The ML training module 230 uses the atmospheric gas columndensity data included in the sparse extraction data 222 as the targetfeature to be predicted by ML model(s) 232-1 during training based onthe input explanatory features. During training, the ML model(s) 232-1learns correlations between the inputted multispectral band data,spatial autocorrelation structure(s)/data, and atmospheric gas columndensity data such that the ML model(s) 232-1 can predict atmospheric gasdensity/concentration values for an atmospheric gas of interest (e.g.,NO2) at data points within an ROI with a higher spatial resolution thanthat provided by the remote sensing devices 108. For example, theatmospheric gas column data taken as input by the ML model(s) 232-1typically has a low spatial resolution of between 3 km to 7 km. However,the multispectral band data and the spatial autocorrelationstructure(s)/data taken as input by ML model(s) 232-1 have a much higherresolution, such as 10 m, 60 m, 750 m, etc. Therefore, by learning thecorrelations between the low spatial resolution atmospheric gas columndata and the high-resolution multispectral band data and spatialautocorrelation structure(s)/data, the ML model(s) 232-1 is trained topredict atmospheric gas column density/concentration values for datapoints at a higher spatial resolution (e.g., 500 m×500 m) than that ofthe input atmospheric gas column data.

In one or more embodiments, the ML training module 230 performs agradient boosting technique to train/generate the ML model(s) 232-1.Gradient boosting is a machine learning technique that provides aprediction model in the form of an ensemble of weak prediction models,such as decision/regression trees. Stated differently, gradient boostingcombines several weak learners in an iterative fashion to yield a singlestrong model. Gradient boosting has three main components, a lossfunction, weak learners, and an additive model. The loss function (e.g.,root mean square error) is used to estimate how “good” the model is atmaking predictions with the given data. For example, if the ML model(s)232-1 is being trained to predict NO2 column density values for an ROI(a regression problem), then the loss function is defined/selected tohelp determine the difference between the predicted NO2 column densityvalues and the observed NO2 column density values. A weak learner is onethat classifies the input data but, in many instances, no better thanrandom guessing. A weak learner is typically a decision/regression tree.An additive model is the iterative and sequential approach of adding thedecision trees one step at a time. Each iteration of adding a decisiontree should reduce the value of the loss function.

For example, the training portion of the sparse extracted dataset 222 isapplied to a first decision tree and the prediction error/loss of thefirst decision tree is determined using the validation portion of thesparse extracted dataset 222. A gradient descent procedure is performedto minimize the error/loss of the first decision tree by adding a seconddecision tree that is built based on the prediction errors/loss of thefirst decision tree's results. Stated differently, the second decisiontree is parameterized, and its parameters are modified such that theresidual loss is reduced while not changing any prior decisions treesThe training portion of the sparse extracted dataset 222 is then appliedto the second decision tree and the output of the second decision treeis added to the output of the first decision tree to correct or improvethe final output of the ML model(s) 232-1. The prediction error/loss ofthe ML model(s) 232-1 including the second decision tree is determinedusing the validation portion of the sparse extracted dataset 222. Anadditional decision tree is added (if needed) based on the predictionerror/loss of current the ML model(s) 232-1 including the seconddecision tree. This process is iteratively performed after a fixednumber of decisions trees are added or until the loss function reachesan acceptable level or no longer improves on the validation dataset atleast by a given threshold. Example hyperparameters for training the MLmodel(s) 232-1 include a learning rate of 0.1, a least squaresregression loss function, a number of estimators set to 50, a maximumdepth of the individual regression estimators set to 5, a minimum numberof samples required to split an internal node set to 7, and a minimumnumber of samples required to be at a leaf node set to 12. Training theML model(s) 232-1 with these hyperparameters achieves an accuracy of 92%or above. However, other hyperparameters are applicable as well.

The result of the training process is a trained ML model(s) 232-2configured to take as input a runtime dataset, such as the denseextracted dataset 224, comprising atmospheric gas column data 208 havinga first spatial resolution and one or more of multispectral data 210 orspatial autocorrelated data 228. The trained ML model(s) 232-2 isfurther configured to process the input runtime dataset and output a setof data points 234 with predicted atmospheric gas concentration valuesfor gas(es) of interest at a second spatial resolution that is greaterthan the first spatial resolution. Stated differently, the set of datapoints 234 includes more atmospheric gas concentration data for the gasof interest than what was provided to the trained ML model(s) 232-2 asinput.

In one or more embodiments, the post-processing module 206 visualizesthe set of data points 234 generated by the GIML module 114 as ahigh-resolution map/raster 116. For example, a point-to-raster module236 of the post-processing module 206 converts the data points to pixelsat a chosen resolution (e.g., 500 m×500 m) by performing a rasterizationoperation that calculates the mean value of data points at the givenspatial scale in “square” pixels. The post-processing module 206, in oneor more embodiments, also implements a low-pass filter for furthersmoothening the rasterized output. Each of the pixels is associated withthe latitude and longitude coordinates and predicted atmospheric gasconcentration value(s) of the corresponding data point. The collectionof pixels is referred to as a “rasterized image”. In one or moreembodiments, the rasterized image is passed into a low-pass filter 238to further reduce noise in the data. The low-pass filer 238 aggregatesthe mean of each pixel with respect to their immediate neighboringpixels. The output of the post-processing module 206 is ahigh-resolution (fine-scale) atmospheric gas map 116 comprising aplurality of pixels for an ROI having predicted atmospheric gasconcentration values for a gas(es) of interest, such as NO2. Thehigh-resolution atmospheric gas map 116, in one or more embodiments, istransmitted to (or otherwise obtained by) one or more other systems 104for presentation to a user, processing/analysis, or the like.

FIG. 3 shows a first atmospheric gas map 302 of an ROI generated by aremote sensing device 108, such as the Sentinel-5P satellite, at itscurrent maximum resolution and a second atmospheric gas map 304 for theROI generated by the mapping system 112 of one or more embodiments. Asshown, the atmospheric gas map 304 generated by the mapping system 112of one or more embodiments has a much higher spatial resolution withmore granular atmospheric gas concentration data, as illustrated by themore refined/granular shading, than the atmospheric gas map 302generated by the remote sensing device 108.

FIG. 4 and FIG. 5 together illustrate an example method 400 forgenerating a high-resolution (fine-scale) atmospheric gas map 116comprising a plurality of data points (pixels) for an ROI havingpredicted atmospheric gas concentration values of a gas or gasses ofinterest. In one or more embodiments, the method 400 initiates at block402 with the mapping system 112 obtaining remote sensing data 110collected by one or more remote sensing devices 108. As described above,the remote sensing data 110 includes, for example, atmospheric gascolumn data 208, multispectral data 210, SAR data 212, and VIIRS data214. At block 404, the mapping system 112 pre-processes the remotesensing data 110 to aggregate remote sensing data 110 collected over agiven temporal window 216 and a spatial window 218 defining an ROI.

At block 406, the mapping system 112 spatially aligns the aggregatedremote sensing data 110 and extracts remote sensing data 110 ofinterest. As described above, the remote sensing data 110, in one ormore embodiments, is represented as a raster. As such, the aggregatedremote sensing data 110 is represented as aggregated spatially alignedrasters. For a plurality of data points (pixel) representing the ROIwithin the spatially aligned rasters, the mapping system 112 extractsremote sensing data 110 of interest, such as the total vertical columnof NO2 (or other gases of interest), the multispectral bands illustratedin Table 1 above, SAR data provided by the VV and VH backscatter bands,and nighttime radiance values. If the mapping system 112 is generatingtraining data, the plurality of data points include a set of randomlyselected sparse data points representing the ROI in the spatiallyaligned rasters. However, if the mapping system 112 is generatingruntime data, the plurality of data points include a set of randomlyselected dense data points representing the ROI in the spatially alignedrasters, where the dense data points have a closer distance to eachother than the sparse data points.

At block 408, the mapping system 112 performs one or more local spatialautocorrelation operations to determine a spatial associationindicator(s) for the extracted SAR data and a spatial associationindicator(s) for the extracted nighttime radiance values. For example,the mapping system 112 calculates a local Moran's I for the extractedSARs data providing a land categorization/classification of eachdatapoint within the ROI and calculates a local Moran's I for theextracted nighttime radiance values providing ageo-physical/socio-economic classification of each datapoint within theROI. The spatial association indicator(s) are stored within a spatialautocorrelated structure(s) 228.

At block 410, the mapping system 112 merges the extracted atmosphericgas column data, extracted multispectral data, and one or more of theland categorization/classification data or thegeo-physical/socio-economic classification data as a single/mergeddataset. If a training and validation dataset are being generated, thesingle dataset can be referred to as a sparse extraction dataset 222 andif a runtime dataset is being generated, the single dataset can bereferred to as dense extraction dataset 224. Multiple instances of theoperations described above with respect to blocks 402 to 410 can be runin parallel to concurrently generate the sparse extraction dataset 222and the dense extraction dataset 224. Alternatively, the sparseextraction dataset 222 and the dense extraction dataset 224 can begenerated at different times. For example, the mapping system 112 cangenerate the dense extraction dataset 224 at various intervals or inresponse to receiving a request to generate high-resolution atmosphericgas maps 116.

At block 412, the mapping system 112 trains and validates one or more MLmodels 232-1 using the sparse extraction dataset 222(training/validation dataset). For example, the mapping system 112implements a gradient boosting technique to train the ML model(s) 232-1with extracted the spectral bands (e.g., see Table 1) and the generatedspatial autocorrelation data as input explanatory features and theextracted the atmospheric gas column density data as the target featureto be predicted by ML model(s) 232-1 during training based on the inputexplanatory features. As described above, during training, the MLmodel(s) 232-1 learns correlations between the inputted multispectralband data, spatial autocorrelation structure(s)/data, and atmosphericgas column density data such that the ML model(s) 232-1 can predictatmospheric gas column density/concentration values of an atmosphericgas of interest for data points within an ROI with a higher spatialresolution than that provided by the remote sensing devices 108.

At block 414, the mapping system 112 inputs a runtime dataset, such asthe dense extraction dataset 224, into the GIML module 114 comprisingthe trained ML models 232-1. At block 416, the GIML module 114 processesthe input and outputs a set of data points 234 with predictedatmospheric gas concentration values at a spatial resolution that isgreater than the spatial resolution of the atmospheric gas data includedin the dense extraction dataset 224. At block 418, the mapping system113 generates one or more high-resolution atmospheric gas maps 116representing the data points 234 as described above with respect to FIG.2 . The process then ends at block 420.

FIG. 6 illustrates an information processing system 600 that can beutilized in embodiments of the present disclosure. The informationprocessing system 600 is based upon a suitably configured processingsystem configured to implement one or more embodiments of the presentdisclosure such as information processing system 102 of FIG. 1 .

Any suitably configured processing system can be used as the informationprocessing system 600 in embodiments of the present disclosure. Thecomponents of the information processing system 600 can include, but arenot limited to, one or more processors or processing units 602, a systemmemory 604, and a bus 606 that couples various system componentsincluding the system memory 604 to the processor 602. The bus 606represents one or more of any of several types of bus structures,including a memory bus or memory controller, a peripheral bus, anaccelerated graphics port, and a processor or local bus using any of avariety of bus architectures. By way of example, and not limitation,such architectures include Industry Standard Architecture (ISA) bus,Micro Channel Architecture (MCA) bus, Enhanced ISA (EISA) bus, VideoElectronics Standards Association (VESA) local bus, and PeripheralComponent Interconnects (PCI) bus.

In one or more embodiments, the main memory 604 includes the mappingsystem 112 and its components described above with respect to FIG. 2 .However, in other embodiments, the mapping system 112 resides within theprocessor 602 or is implemented as a separate hardware component throughcircuitry. The system memory 604 can also include computer systemreadable media in the form of volatile memory, such as random accessmemory (RAM) 608 and/or cache memory 610. The information processingsystem 600 can further include other removable/non-removable,volatile/non-volatile computer system storage media. By way of exampleonly, a storage system 612 can be provided for reading from and writingto a non-removable or removable, non-volatile media such as one or moresolid state disks and/or magnetic media (typically called a “harddrive”). A magnetic disk drive for reading from and writing to aremovable, non-volatile magnetic disk and/or optical disk drive forreading from or writing to a removable, non-volatile optical disk suchas a CD-ROM, DVD-ROM or other optical media can be provided. In suchinstances, each can be connected to the bus 606 by one or more datamedia interfaces. The memory 604 can include at least one programproduct having a set of program modules that are configured to carry outthe functions of an embodiment of the present disclosure.

Program/utility 614, having a set of program modules 616, may be storedin memory 604 by way of example, and not limitation, as well as anoperating system, one or more application programs, other programmodules, and program data. Each of the operating system, one or moreapplication programs, other program modules, and program data or somecombination thereof, may include an implementation of a networkingenvironment. Program modules 616 generally carry out the functionsand/or methodologies of embodiments of the present disclosure.

The information processing system 600 can also communicate with one ormore external devices 618 such as a keyboard, a pointing device, adisplay 620, etc.; one or more devices that enable a user to interactwith the information processing system 600; and/or any devices (e.g.,network card, modem, etc.) that enable computer system/server 600 tocommunicate with one or more other computing devices. Such communicationcan occur via I/O interfaces 622. Still yet, the information processingsystem 600 can communicate with one or more networks such as a localarea network (LAN), a general wide area network (WAN), and/or a publicnetwork (e.g., the Internet) via network adapter 624. As depicted, thenetwork adapter 624 communicates with the other components ofinformation processing system 600 via the bus 606. Other hardware and/orsoftware components can also be used in conjunction with the informationprocessing system 600. Examples include, but are not limited tomicrocode, device drivers, redundant processing units, external diskdrive arrays, RAID systems, tape drives, and data archival storagesystems.

As will be appreciated by one of ordinary skill in the art, in view ofthe discussions herein, aspects of the present invention may be embodiedas a system, method, or computer program product.

Accordingly, one or more aspects of the present invention may take theform of an entire hardware embodiment, an entire software embodiment(including firmware, resident software, micro-code, etc.), or anembodiment combining software and hardware aspects that may allgenerally be referred to herein as a “circuit”, “module”, or “system”.Furthermore, parts of the present invention may take the form of acomputer program product embodied in one or more computer-readablemedium(s) having the computer readable program code embodied thereon.

A system 600 may utilize any combination of computer-readable medium(s).The computer-readable medium may be a computer-readable signal medium ora computer-readable storage medium. A computer-readable storage mediummay be, for example, but not limited to, an electronic, magnetic,optical, electromagnetic, infrared, or semiconductor system, apparatus,or device, or any suitable combination of the preceding.

More specific examples (a non-exhaustive list) of the computer-readablestorage medium would include the following: an electrical connectionhaving one or more wires, a portable computer diskette, a hard disk, arandom access memory (RAM), a read-only memory (ROM), an erasableprogrammable read-only memory (EPROM or Flash memory), an optical fiber,a portable compact disc read-only memory (CD-ROM), an optical storagedevice, a magnetic storage device, or any suitable combination of thepreceding. In the context of this document, a computer-readable storagemedium may be any tangible medium that can contain or store a programfor use by or in connection with an instruction execution system,apparatus, or device.

A computer-readable signal medium may include a propagated data signalwith computer readable program code embodied therein, for example, inbaseband or as part of a carrier wave. Such a propagated signal may takeany of a variety of forms, including, but not limited to,electromagnetic, optical, or any suitable combination thereof. Acomputer-readable signal medium may be any computer-readable medium thatis not a computer-readable storage medium, and that can communicate,propagate, or transport a program for use by or in connection with aninstruction execution system, apparatus, or device.

Program code embodied on a computer-readable medium may be transmittedusing any appropriate medium, including but not limited to wireless,wireline, optical fiber cable, radio frequency (RF), etc., or anysuitable combination of the preceding.

Computer program code for carrying out operations for aspects of thepresent invention may be written in any combination of one or moreprogramming languages, including an object-oriented programming languagesuch as Java, Smalltalk, C++, or the like, and conventional proceduralprogramming languages, such as the “C” programming language or similarprogramming languages. According to various embodiments of theinvention, the program code may execute entirely on a user's computer,partly on a user's computer, as a stand-alone software package, partlyon a user's computer and partly on a remote computer or entirely on aremote computer or a server. In the latter scenario, the remote computeror the server may be connected to the user's computer through any typeof network, including one or more of a local area network (LAN), awireless communication network, a wide area network (WAN), or aconnection may be made to an external computer (for example, through theInternet using an Internet Service Provider).

Aspects of the present invention have been discussed above withreference to flowchart illustrations and/or block diagrams of methods,apparatus (systems), and computer program products according to variousembodiments of the invention. One or more of the operations illustratedin any of the flowchart illustrations can be performed in a differingorder. Other operations, for example, can be added, modified, enhanced,condensed, integrated, or consolidated. Variations thereof areenvisioned and are intended to fall within the scope of the appendedclaims. Also, each block of the flowchart illustrations and/or blockdiagrams and combinations of blocks in the flowchart illustrations andblock diagrams can be implemented by computer program instructions.These computer program instructions may be provided to a processor of ageneral-purpose computer, special purpose computer, or otherprogrammable data processing apparatus to produce a machine, such thatthe instructions, which execute via the processor of the computer orother programmable data processing apparatus, create means forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

These computer program instructions may also be stored in acomputer-readable medium that can direct a computer, other programmabledata processing apparatus, or other devices, to function in a particularmanner, such that the instructions stored in the computer-readablemedium produce an article of manufacture including instructions whichimplement the function/act specified in the flowchart and/or blockdiagram block or blocks.

The computer program instructions may also be loaded onto a computer,other programmable data processing apparatus, or other devices, to causeoperational steps to be performed on the computer, other programmableapparatus, or other devices, to produce a computer-implemented process(or method) such that the computer instructions which execute on thecomputer or other programmable apparatus provide processes forimplementing the functions/acts specified in the flowchart and/or blockdiagram block or blocks.

The terminology used herein is to describe particular embodiments onlyand is not intended to be limiting of the invention.

As used herein, the singular forms “a”, “an” and “the” are intended toinclude the plural forms as well, unless the context clearly indicatesotherwise. It will be further understood that the terms “comprises”and/or “comprising,” when used in this specification, specify thepresence of stated features, integers, steps, operations, elements,and/or components, but do not preclude the presence or addition of oneor more other features, integers, steps, operations, elements,components, and/or groups thereof.

The terms “a” or “an,” as used herein, are defined as one or more thanone. The term plurality, as used herein, is defined as two or more thantwo. The term “another”, as used herein, is defined as at least a secondor more. The terms “including” and “having”, as used herein, are definedas comprising (i.e., open language). The term “coupled”, as used herein,is defined as “connected,” although not necessarily directly and notnecessarily mechanically. The term “configured to” describes thehardware, software, or a combination of hardware and software that isadapted to, set up, arranged, built, composed, constructed, designed, orthat has any combination of these characteristics to carry out a givenfunction. The term “adapted to” describes the hardware, software, or acombination of hardware and software capable of performing, able toaccommodate the performance of, that is suitable to perform, or that hasany combination of the characteristics mentioned above to perform agiven function.

The description of the present invention has been presented for purposesof illustration and description but is not intended to be exhaustive orlimited to the invention in the form disclosed. Many modifications andvariations will be apparent to those of ordinary skill in the artwithout departing from the scope of the invention. Each embodiment waschosen and described to best explain the principles of the invention andthe practical application and to enable others of ordinary skill in theart to understand the invention for various embodiments with variousmodifications as are suited to the particular use contemplated.

What is claimed is:
 1. A method for generating one or morehigh-resolution atmospheric gas concentration maps usinggeography-informed machine learning, the method comprising: obtaining aremote sensing dataset constrained by at least one temporal window andat least one spatial window defining a first geographic area, the remotesensing dataset comprising at least a first set of atmospheric gasconcentration data for a plurality of atmospheric gases; generating atraining dataset based on the remote sensing dataset; and training amachine learning model with the training dataset to predict a pluralityof atmospheric gas concentration values for at least one atmospheric gasof the plurality of atmospheric gases in a given geographic area andwith a spatial resolution that is greater than a spatial resolution ofatmospheric gas concentration data provided as an input to the machinelearning module.
 2. The method of claim 1, further comprising:processing, by the trained machine learning model, an input datasetcomprising a second set of atmospheric gas concentration data for the atleast one atmospheric gas, the second set of atmospheric gasconcentration data being associated with a second geographic area andhaving a first spatial resolution; and generating, by the trainedmachine learning model based on processing the input dataset, aplurality of predicted atmospheric gas concentration values for the atleast one atmospheric gas, wherein the plurality of predictedatmospheric gas concentration values has a second spatial resolutionthat is greater than the first spatial resolution.
 3. The method ofclaim 1, wherein the remote sensing dataset further comprises a firstset of multispectral data associated with the first geographic area andat least one of a first set of synthetic aperture radar data or a firstset of nighttime radiance data associated with the first geographicarea.
 4. The method of claim 3, wherein the remote sensing datasetcomprises a plurality of rasters including a first set of rastersrepresenting the first set of atmospheric gas concentration data, asecond set of rasters representing the first set of multispectral data,and at least one of a third set of rasters representing the first set ofsynthetic aperture radar data or a fourth set of rasters representingthe first set of nighttime radiance data.
 5. The method of claim 4,wherein generating the training dataset comprises: based on the at leastone temporal window and the at least one spatial window, spatially andtemporally aligning a plurality of raster subsets including a subset ofthe first set of rasters, a subset of the second set of rasters, and asubset of at least one of the third set of rasters or the fourth set ofrasters; for each raster point in a set of randomly selected rasterpoints constrained by the at least one spatial window, extracting remotesensing data of interest from the plurality of raster subsets, theextracted remote sensing data including extracted atmospheric gasconcentration data for the at least one atmospheric gas, extractedmultispectral data, and at least one of extracted synthetic apertureradar data or extracted nighttime radiance data; and storing at leastthe extracted atmospheric gas concentration data and the extractedmultispectral data as the training dataset.
 6. The method of claim 5,wherein generating the training dataset further comprises: determiningone or more local spatial association indicators for the at least one ofthe extracted synthetic aperture radar data or the extracted nighttimeradiance data, wherein the one or more local spatial associationindicators provide a set of spatially autocorrelated land useclassifications for each raster point in the set of randomly selectedraster points; and storing the set of spatially autocorrelated land useclassifications as part of the training dataset.
 7. The method of claim6, wherein determining one or more local spatial association indicatorscomprises calculating a local Moran's Index for the at least one of thesecond set of synthetic aperture radar data or the second set ofnighttime radiance data.
 8. The method of claim 6, wherein training themachine learning model comprises performing gradient boosting using theextracted multispectral data and the set of spatially autocorrelatedland use classifications as explanatory variables and the extractedatmospheric gas concentration data as a target feature to be predictedby the machine learning model.
 9. An information processing system forgenerating one or more high-resolution atmospheric gas concentrationmaps using geography-informed machine learning, the informationprocessing system comprising: a processor; memory communicativelycoupled to the processor; and an atmospheric gas mapping unitcommunicatively coupled to the processor and the memory, wherein theatmospheric gas mapping unit: obtains a remote sensing datasetconstrained by at least one temporal window and at least one spatialwindow defining a first geographic area, the remote sensing datasetcomprising at least a first set of atmospheric gas concentration datafor a plurality of atmospheric gases; generates a training dataset basedon the remote sensing dataset; and trains a machine learning model withthe training dataset to predict a plurality of atmospheric gasconcentration values for at least one atmospheric gas of the pluralityof atmospheric gases in a given geographic area and with a spatialresolution that is greater than a spatial resolution of atmospheric gasconcentration data provided as an input to the machine learning module.10. The information processing system of claim 9, wherein the trainedmachine learning model: processes an input dataset comprising a secondset of atmospheric gas concentration data for the at least oneatmospheric gas, the second set of atmospheric gas concentration databeing associated with a second geographic area and having a firstspatial resolution; and generates, based on processing the inputdataset, a plurality of predicted atmospheric gas concentration valuesfor the at least one atmospheric gas, wherein the plurality of predictedatmospheric gas concentration values has a second spatial resolutionthat is greater than the first spatial resolution.
 11. The informationprocessing system of claim 9, wherein the remote sensing dataset furthercomprises a first set of multispectral data associated with the firstgeographic area and at least one of a first set of synthetic apertureradar data or a first set of nighttime radiance data associated with thefirst geographic area.
 12. The information processing system of claim11, wherein the remote sensing dataset comprises a plurality of rastersincluding a first set of rasters representing the first set ofatmospheric gas concentration data, a second set of rasters representingthe first set of multispectral data, and at least one of a third set ofrasters representing the first set of synthetic aperture radar data or afourth set of rasters representing the first set of nighttime radiancedata.
 13. The information processing system of claim 12, wherein theatmospheric gas mapping unit generates the training dataset by:spatially and temporally aligning a plurality of raster subsetsincluding a subset of the first set of rasters, a subset of the secondset of rasters, and a subset of at least one of the third set of rastersor the fourth set of rasters based on the at least one temporal windowand the at least one spatial window; for each raster point in a set ofrandomly selected raster points constrained by the at least one spatialwindow, extracting remote sensing data of interest from the plurality ofraster subsets, the extracted remote sensing data including extractedatmospheric gas concentration data for the at least one atmospheric gas,extracted multispectral data, and at least one of extracted syntheticaperture radar data or extracted nighttime radiance data; and storing atleast the extracted atmospheric gas concentration data and the extractedmultispectral data as the training dataset.
 14. The informationprocessing system of claim 13, wherein the atmospheric gas mapping unitgenerates the training dataset further by: determining one or more localspatial association indicators for the at least one of the extractedsynthetic aperture radar data or the extracted nighttime radiance data,wherein the one or more local spatial association indicators provide aset of spatially autocorrelated land use classifications for each rasterpoint in the set of randomly selected raster points; and storing the setof spatially autocorrelated land use classifications as part of thetraining dataset.
 15. The information processing system of claim 14,wherein determining one or more local spatial association indicatorscomprises calculating a local Moran's Index for the at least one of thesecond set of synthetic aperture radar data or the second set ofnighttime radiance data.
 16. The information processing system of claim14, wherein the atmospheric gas mapping unit trains the machine learningmodel by performing gradient boosting using the extracted multispectraldata and the set of spatially autocorrelated land use classifications asexplanatory variables and the extracted atmospheric gas concentrationdata as a target feature to be predicted by the machine learning model.17. A method for generating one or more high-resolution atmospheric gasconcentration maps using geography-informed machine learning, the methodcomprising: obtaining a remote sensing dataset constrained by at leastone temporal window and at least one spatial window defining a firstgeographic area, the remote sensing dataset comprising at least a firstset of atmospheric gas concentration data for a plurality of atmosphericgases; generating a training dataset based on the remote sensingdataset; training a machine learning model with the training dataset;processing, by the trained machine learning model, an input datasetcomprising a second set of atmospheric gas concentration data for atleast one atmospheric gas of the plurality of atmospheric gases, thesecond set of atmospheric gas concentration data being associated with asecond geographic area and having a first spatial resolution; andpredicting, by the trained machine learning module based on processingthe input dataset, a plurality of atmospheric gas concentration valuesfor the at least one atmospheric gas, wherein the plurality of predictedatmospheric gas concentration. values has a second spatial resolutionthat is greater than the first spatial resolution.
 18. The method ofclaim 17, wherein the remote sensing dataset further comprises a firstset of multispectral data associated with the first geographic area andat least one of a first set of synthetic aperture radar data or a firstset of nighttime radiance data associated with the first geographicarea.
 19. The method of claim 18, wherein the remote sensing datasetcomprises a plurality of rasters including a first set of rastersrepresenting the first set of atmospheric gas concentration data, asecond set of rasters representing the first set of multispectral data,and at least one of a third set of rasters representing the first set ofsynthetic aperture radar data or a fourth set of rasters representingthe first set of nighttime radiance data.
 20. The method of claim 19,wherein generating the training dataset comprises: based on the at leastone temporal window and the at least one spatial window, spatially andtemporally aligning a plurality of raster subsets including a subset ofthe first set of rasters, a subset of the second set of rasters, and asubset of at least one of the third set of rasters or the fourth set ofrasters; for each raster point in a set of randomly selected rasterpoints constrained by the at least one spatial window, extracting remotesensing data of interest from the plurality of raster subsets, theextracted remote sensing data including extracted atmospheric gasconcentration data for the at least one atmospheric gas, extractedmultispectral data, and at least one of extracted synthetic apertureradar data or extracted nighttime radiance data; and storing at leastthe extracted atmospheric gas concentration data and the extractedmultispectral data as the training dataset.